Does happiness influence investment in financial assets?

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Does happiness influence investment in financial

assets?

Jasper Broere

S3175227

j.m.broere@student.rug.nl

MSc Finance

Faculty of Economics and Business

Supervisor:

Steffen Eriksen

Abstract

Due to the increasing personal responsibility among individuals regarding financial resources and thus financial decision making, this paper investigates the relationship between happiness and investments in financial assets. Using data of the LISS panel administered by CentERdata, this paper shows that happiness has a positive but non-significant effect on the propensity to invest in financial assets. The results also show that the amount of wealth invested is positively but non-significantly influenced by an individual’s level of happiness.

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1. Introduction

Throughout history the average age of the human population has risen considerably. And until the twentieth century it was not ordinary for an individual to reach old age (Riley, 2001). This increase in life expectancy implies that individuals have a longer retirement period than a few decades ago, which asks for more careful use of their financial resources. Furthermore, due to market liberalization and structural reorganization of the social security and pension system, the decision-making process shifts from governments and employers to individuals. This means that the responsibility for an individual’s long-term financial security lays with himself (Van Rooij, Ludardi, and Alessie, 2012). Therefore, it is important for both policy makers and economists to have a broader understanding of the decision-making process of individuals regarding financial investments.

The empirical literature shows that there is a wide variation in household portfolio regarding financial assets (Zhang, 2017; Bucciol and Miniaci, 2014; Kennickel and Starr-McCluer, 1997). Furthermore, the empirical literature has recognized some basic determinants regarding individual investment. Especially the nonfinancial income has a huge impact on stock-market participation (Vissing-Jorgensen 2002). Also, an investor’s financial education has an impact on its stock-market participation (Cooper and Zhu, 2013). Bernheim and Garrett (1996) find that employer based financial/retirement education strongly influences the household financial behavior. Additionally, some studies found relations between a household’s stock market participation and health status (Rosen and Wu 2003), Social and cultural interactions (Grinblatt and Keloharju 2001), and permanent income risk (variability of shocks to income that have permanent effect) (Angerer and Lam 2009). This paper attempts to explain a possible correlation between and individuals perceived level of happiness and their investments in financial assets.

There are numerous psychological and neurological studies that show that emotions play an important role in the decision-making process when looking at investment behavior (Lyubomirsky, King, and Diener, 2005; Van Winden, Krawczyk, and Hopfensitz, 2008). However, in the psychologic literature, two opposing models regarding the relation between different mood states and risk-taking behavior exist. The first one is the Mood Maintenance Hypothesis (MMH), which suggests that individuals that enjoy a positive emotional state, want to protect their present emotional state; thus, tend to be more reluctant to gamble (Isen and Patrick, 1983). The opposing model, the Affect Infusion Model (AIM), argues that a positive emotional state increases the tendency to take risk, while individuals in a negative emotional state experience more risk-averse behavior (Forgas, 1995).

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In order to test if an individual’s happiness influences his or her decision-making process regarding investing in financial assets, data of the LISS (Longitudinal Internet Studies for the Social Sciences) panel administered by CentERdata (Tilburg University, The Netherlands) is analyzed. The LISS panel is based on representative sample of Dutch households drawn from the population register by statistics Netherlands. The Panel consists of 4,500 Dutch households, comprising 7,000 individuals. The panel collects data on an array of different subject such as health, religion, education, and politics. It also covers topics that are useful for this study like economics situation and certain personality traits.

Due to the fact that the sample used in this study consists only of individuals that do report their investments, as such, it most likely suffers from a sample selection bias, which makes the coefficients and standard errors biased and inconsistent. As the magnitude and direction of the bias are influenced by the relationship between happiness and investing it is, again, not possible to say whether this is positive or negative. In order to make a correction for this sample selection bias, the two-step model by Heckman is conducted. Furthermore, happiness may be an endogenous variable, which causes the coefficients to be biased and inconsistent. A possible solution to this problem is to add an instrumental variable or a good proxy for unobserved variables to the model. However, as the happiness variable used in this model is already a proxy of “real” happiness it seems a bit odd to use a proxy of a proxy. Furthermore, According to Praag (2007), there is not yet a representative instrumental variable due to the complex nature of happiness. Therefore, instead of using an instrumental variable for happiness, various proxies for unobservable characteristics are added to the model in order to extracts the most important factors that are in the error term. First, a proxy for the effect of the weather is added, as the weather is presumed to have an effect on both an individual’s happiness and stock market returns (Cunningham, 1979; Hirshleifer and Shumway, 2003). Second, a proxy for the effect of optimism is added, as happier individuals are more optimistic; hence might have bigger expectations for future returns (Arkes, Herren, and Isen, 1988). Third, a proxy for the effect of risk tolerance is added, as happier individuals are presumed to have a different risk appetite than less happy individuals (Johnson and Tversky ,1983).

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2. Literature review and hypotheses

This section will give an overview of the related literature and present the hypothesis that are tested during this study. First, there will be a brief discussion of the happiness index. Second, there will be an explanation of what happiness is and where it derives from. Third, an explanation for the measurement of happiness will be given. Fourth, there will be an explanation of the equity premium puzzle. Last, there will be an explanation of the causal relationship between happiness and other variables, and the hypotheses will be presented. 2.1 World happiness index

The world happiness index is a global index which ranks 156 countries by their perceived well-being. When looking at the world happiness index, the Netherlands has not left the top 10 of the happiest countries in the world since the start of the index (Helliwell, Layard, and Sachs, 2018). This might indicate that the average perceived level of happiness of the Dutch population is relatively high. Therefore, it might be harder to test the differences between happier and unhappier individuals, as most individuals are relatively happy.

2.2 Defining happiness

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intentional activities (Lykken and Tellegen, 1996; Waller et al., 1990; lyubomirsky, King, and Diener, 2005). This would mean that each individual has a base level of happiness that is hardwired in their DNA but that their total happiness fluctuates around this base due to positive and negative circumstances, and intentional activities. This can explain why some people seem to be in an overall happier state than others under the same conditions.

2.3 Measuring happiness

In the empirical literature there are multiple approaches which measure the happiness of an individual. The most standard approach is through a single closed survey question. An example of such a question is:

“Taking all things together, how happy would you say you are with your life as a whole?” with five response options: very happy, happy, neutral, unhappy, very unhappy.

When using the most standard approach it is relatively easy to obtain the data on a large scale. However, each individual undergoes his own personal process in rating his or her happiness. As an alternative to the standard approach some researchers used different approaches on how to measure happiness. In a study by, Webb (1915), on character and intelligence, teachers rate the happiness of their student on a scale of -3 to 3. Where -3 indicates “lowest as compared to average”, and 3 indicates “very high compared to average”. Shaefer and Bayley (1963), in a study regarding maternal and child behavior measured the happiness of children from infancy through adolescence. The happiness of the child is measured through interview and observation by two independent interviewers. Both indicating the happiness of the child on a scale of 1 to 7. The total level of happiness is the average of the ratings of the two independent interviewers. The methods adopted by Shaefer and Bayley, and the method adopted by Webb, minimize differences in the personal evaluation process of happiness by only letting a selected few evaluate the happiness of the individuals by. However, this is a very time consuming and costly process.

In a study by, Tobacyk (1981), participants rated their moods three times a day, for 33 consecutive days, using a personal feeling scale consisting of 16 questions. The questions had to be answered on a scale of 0-9. Diener, Emmons, Larsen, and Griffin (1985) developed the satisfaction with life scale, which measures happiness on the basis of five closed survey question on a scale of 1 to 7. Where 1 indicates “strongly disagree” and 7 indicates “strongly agree”. The value for happiness is computed by taking the average of the five questions. The satisfaction with life scale and the personal feeling scale, compared to standard approach, might be more precise as happiness is measured on the basis of five and sixteen questions instead of one. However, this method is more time consuming and each individual still undergoes his own personal process in rating his or her happiness.

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Indicating that assessing an individual’s happiness by a peer is not an appropriate approach for measuring and individual’s happiness. Direct self-reporting questions on the other hand are an appropriate approach in measuring an individual’s happiness (Lyubomirsky and Lepper, 1999; Diener, 1994; Sandvik, Diener, and Seidlitz, 1993).

The proxies for happiness obtained by the survey questioned have been interchangeably termed “happiness”, “subjective well-being”, and “Life satisfaction” (e.g. Guven, 2012; Farid and Lazarus, 2008; Proto and Rustichini, 2015). In this study the proxy obtained from the survey questioned is named happiness.

When trying to explain the causal relation between happiness and investing one must take into account the possible endogeneity of the happiness variable. There might be unobservable characterizes that influence both happiness and investing simultaneous. This may lead to incorrectly identifying a causal relationship between happiness and investing, when the actual relationship is due to another variable. A possible solution to mitigate this problem would be to add as many observable characteristics in order to extract factors that are in the error term. However, this often leads to shrinking the sample, which causes to devaluate the results. Despite the implementation of this method there still might be endogeneity in the model. In order to fix this problem of endogeneity an instrumental variable for happiness or a good proxy for the unobserved factors must be added to the model. However, this is not an easy task. According to Praag (2007), there is not yet a representative instrumental variable due to the complex nature of happiness. Although some more recent papers used different instrumental variables to measure happiness. Some examples of these instrumental variables are regional sunshine, family relations, the happiness index of a city, ecological environment, and leisure entertainment (Guven and Hoxha, 2015; Delis and Mylonidis, 2015; Rao, Mei, And Zhu, 2015). 2.4 Equity premium puzzle

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In the pursuit of finding the cause of happiness scholars have studied numerous variables that could explain this. The first investigations regarding the cause of happiness where focused on demographic- and life status variables such as, gender, age, race, health, and marital state. Woody and Green (2001) found supporting evidence that race and gender have an influence on an individual’s happiness. Brown (2000) finds that married couples are less depressed than their cohabitating counterparts. According to Diener, Suh, Lucas, and Smith (1999) scholars have estimated that the variance in happiness is caused by these variables for 8% to 15%. Because there is still a lot of variance in happiness unexplained scholars kept looking into different variables such as, wealth, income, social relationships, and employment. Blanchflower and Oswald (2004) report that money buys happiness. Meaning that there is a positive correlation between wealth and happiness. This implies that wealthier individuals are happier than poorer individuals. Easterlin (1995) finds that although one average at a given time an individual with a higher income is happier than an individual with a lower income, raising the income of all does not increase the happiness of all. This may be caused because individuals compare their income to the income of other people in society or because they may derive happiness from their purchasing power. Aside from wealth and income, having friends and a strong social circle are believed to be determinant for happiness. Diener and Seligman (2002) compared in their study very happy individuals with average and very unhappy individuals. They report that the very happy group has stronger romantic and social relationships than the less happy group. This is supported by Helliwell and Putnam (2004) which find that marriage and family, ties to friends and neighbors, workplace ties, civic engagement, trustworthiness and trust all appear to be related to happiness. Clark and Oswald (1994) find in a study on the British population that individuals that are unemployed have much lower levels of mental happiness than individuals that are in the work force.

In the psychologic literature there exist two opposing models regarding the relation between different mood states and risk-taking behavior. The first one is the Mood Maintenance Hypothesis (MMH), which suggests that individuals that are in happy emotional state want to protect their present emotional state; thus, tend to be more reluctant to gamble (Isen and Patrick, 1983). According to Delis and Mylonidis (2015), happier individuals are less likely to invest in risky assets. Guven and Hoxha (2015), find that happy people are less likely to own stocks or bonds. Both these studies support the mood maintenance hypothesis.

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It shows that there are a lot of different variables that have an effect on an individual’s happiness, but this is not a one-way street. Happiness has an influence on a variety of variables and is not only the output of some other variable. According to lyubomirsky, King, and diener (2005) happiness leads to, better health, better social relationships, better work performance, and more ethical behavior.

In addition, there are numerous of studies that show that mood can influence the decision-making process of an individual. Isen (2001) finds that a positive mood fosters clear-headed, well-organized, open-minded, flexible problem solving and thinking. According to an experimental study by Van Winden, Krawczyk, and Hopfensitz (2011) emotions play a significant role with regards to investment decisions. Furthermore, psychological and neurological studies show that emotions play an important role in the decision-making process when looking at investment behavior (Lyubomirsky, King, and Diener, 2005; Van Winden, Krawczyk, and Hopfensitz, 2008).

The findings that suggest emotions play a pivotal role in the decision-making process and the existence of the equity premium puzzle, opens the possibility that there is a relationship between happiness and an individual’s decision to invest in financial assets, or not. In order to determine if there might be a difference regarding the investment decision-making process between happy and unhappy individuals the following hypothesis is tested

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3. Data

This section gives a presentation of the data that is used during this study in order to determine if the level of happiness of an individual has an effect on the propensity to invest in financial assets, and if the level of happiness of an individual increases the amount invested in financial assets. First, there will be an explanation as to where the data is coming from, and which different panels are used in determining the effects of happiness on financial assets. Secondly, the different variables will be explained. Thirdly, a description of the statistics will be presented. Lastly, a correlation matrix is presented that shows the results of the correlations among the variables used in the different estimation models.

3.1 sample

This research paper uses the data of the LISS (Longitudinal Internet Studies for the Social Sciences) panel administered by CentERdata (Tilburg University, The Netherlands). The LISS panel is based on a true probability sample of households drawn from the population register by statistics Netherlands.1_{The Panel consists of 4,500 Dutch households, comprising 7,000}

individuals. Every month each panel member completes an online questionnaire. Part of the interview time available in the LISS panel is reserved for the LISS core study. This longitudinal study is designed to follow changes in the life course and living conditions of the panel members and is repeated yearly. The panel collects data on an array of different subject such as health, religion, education and politics. It also covers topics that are useful for this study like economic situation and certain personality traits of a panel member. Next to their core study the LISS also provides a variety of single wave survey modules regarding different topics that can be used alongside the core study.

For this study several datasets will be used. In order to be able to measure an individual’s happiness, the first wave regarding economic conditions and indicators is used. Of the 7,428 panel that where selected for the selection question, 5,316 of the panel members answered the question. This survey was administered in the month November of the year 2010 and was repeated in the months February and June of the year 2011.

For the measure of an individual’s investments in financial assets the second wave regarding the economic situation assets study, which is part of the LISS core study, is selected. This questionnaire took place in the month June of the year 2010 and was repeated for non-responders in the month July of the year 2010. In the end, of the 8,021 selected household members 5,536 completed the full survey. The LISS core study also includes variables such as living situation and household position which can be used as control variables. Next to the core study there is a dataset with background variables that is generated every month. This dataset is used to control for socio-economic variables such as, gender, age, having children, marital

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state, education, employment, retirement, and income. In order to be able to control for more specific control variables such as, the weather, optimism, and risk tolerance, various single wave questionnaires are conducted. After all the different data sets where collected and merged together into on bigger dataset there remained a total of 755 observations.

Although there is data on the happiness variable on three different points in time, this study only uses the happiness survey of November 2010. This study excludes the happiness survey of February and June 2011. This is because the survey regarding the economic situation of an individual takes place every two year and so there is no data on this topic for the year 2011. Furthermore, combining the happiness surveys of February and June 2011 with the economic situation data of 2012 is not considered as an option because there need to be made assumptions regarding the stationarity of the happiness variables.2_{When combining the happiness}

questionnaire of November 2010 and the economic situation questionnaire of June 2010, no assumptions regarding happiness need to be made. However, this study does assume that economic situation variables of June 2010 are relatively stationary. Figures 1 reports the AEX index over the period 2010, which gives us a rough estimate of the difference in financial assets between June and November 2010.

Fig. 1. AEX Index 2010.

3.2 Dependent variable (Investment in financial assets)

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logarithm of the individuals’ amount invested in financial assets is taken, on condition that an individual has investments in financial assets.3_{The natural logarithm is taken so that the}

function gives outcomes in relative change instead of actual change. Outcomes in relative change are easier to understand and interpret than those in absolute change.

3.3 Explanatory variable (Happiness)

As mentioned before, a wide variety of phycological studies exist, showing how mood can influence the decision-making process of an individual. Therefore, in order to explain differences in the investment in financial assets among individual investors, the happiness variable will be used. The explanatory variable, happiness, is measured by the LISS on a scale of 1 to 5 with the following question: “Which of these sentences best describes your mood at this moment?”. Whereby 1 stands for “I feel great today” and 5 stands for “I feel very bad today”. Although this question is about a particular moment in time this study assumes that an individual’s subjective well-being at the time of filling in the questionnaire is a good proxy of and individuals’ average level of subjective well-being. This is a reliable variable as long as the deviation from the average level of happiness of an individual is randomly distributed among the sample.

3.4 Control variables

3.4.1 Socio-economic variables

In this study there will be controlled for the standard socio-economic variables such as gender, age, having children, marital state, education, employment, retirement, and income. As age and income are continuous variable the data regarding these variables do not require any alterations before they can be used. Alterations to the data are required for the variables gender, having kids, marital state, employment and retirement. Therefore, dummy variables are created for these variables whereby 1 stand for male, having kids, being married, being employed, and being retired, respectively, and 0 otherwise. For education the LISS core study makes a distinction between different educational categories on a of 1 to 6. The six categories are as follows: Primary school, intermediate secondary education, higher secondary education, intermediate vocational education, higher vocational education, and university.

3_{Taking the logarithm can help rescale the data in order to overcome heteroscedasticity by making the variance}

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3.4.2 The weather

Cunningham (1979) finds that sunshine and temperature were significantly related to self-reports of mood. Cao and Wei (2005) examined many stock markets around the world and found a statistically, negative correlation between temperature and returns across the whole range of temperature. Hirshleifer and Shumway (2003) find that there is a correlation between morning sunshine in the city of a country’s leading stock exchange and daily market index return. Because of the correlation between sunshine and mood and the correlation between sunshine and stock exchanges, there might also be a correlation between the weather and stock market participation. The perceived weather variable is measured on a scale of 1 to 5, whereby 1 stand for “Very nice” and 5 stands for “Not nice at all”.

3.4.3 Optimism

According to Arkes, Herren, and Isen (1988) an individual that is in a state of positive affect demonstrates more willingness to pay a larger sum of money for a lottery ticket. It might be possible that more optimistic individuals are more likely to invest in financial assets due to their more optimistic view of future outcomes. Therefore, a control variable that captures an individual’s optimism is created. The LISS measures optimism on a scale of 1 to 5 with the following question: “how do you generally feel compared to other people?”. Whereby 1 stand for “I am a much more positive person than other people” and 5 stands for “I am a much less positive person than other people”.

3.4.4 Risk tolerance

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3.5 Summary statistics

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* Indicates that the original variable has been altered in such a way that 1=5, 2=4, 3 stays 3, 4=2, and 5=1.

Table 1. Summary statistics

VARIABLES Obs. Mean Std. Dev. Min Max

Dependent variable

Invests in financial assets 755 0.486 0.500 0 1

Log financial assets 367 9.798 1.947 0 19.140

Explanatory variable Happiness* ₇₅₅ _3.473 _0.646 ₁ ₅ Control variables Male 755 0.641 0.480 0 1 Age 755 55.96 14.57 16 92 Children 755 0.346 0.476 0 1 Married 755 0.657 0.475 0 1 Education 754 4.129 1.486 1 6 Employed 755 0.538 0.499 0 1 Retired 755 0.298 0.458 0 1

Log net income 621 7.512 0.594 3.912 9.616

House 755 0.477 0.500 0 1

Weather* ₇₅₅ _2.934 _0.802 ₁ ₅

Risk tolerance 755 1.377 1.826 0 5

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3.6. Correlations

Table 2 represents a correlation matrix that shows the results of the correlations among the variables used in the different estimation models. This matrix offers some useful insight on the relationship different variables have with each other. When observing the correlation matrix there seems to be a positive yet non-significant correlation among the dependent and independent variable. This would imply that happier individuals don’t necessarily invest more in financial assets than less happier individuals. When looking at the correlation between the dependent variables and the control variables, a negative and statistically significant correlation between financial assets and being a male (-0.105) can be found. This would imply that an individual is less likely to invest in financial assets if he is a male. Although this correlation isn’t that strong as this correlation coefficients doesn’t surpass -0.105.

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Table 2. Correlation coefficient among variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) Financial assets (1) 1.000 Happiness (2) -0.050 1.000 Male (3) -0.110** _0.083 _1.000 Age (4) 0.061 0.079 0.003 1.000 Children (5) -0.041 -0.012 0.016 -0.398*** _1.000 Married (6) -0.054 0.091* _0.145*** _0.233*** _0.217*** _1.000 Education (7) -0.077 -0.017 0.174*** _-0.114** _0.055 _0.106* _1.000 Employed (8) -0.070 0.030 -0.016 -0.624*** _0.372*** _-0.078 _0.142** _1.000 Retired (9) 0.059 -0.021 0.047 0.686*** _-0.392*** _0.151*** _-0.121** _-0.764*** _1.000

Log net income (10) -0.044 0.122** _0.416*** _0.180*** _-0.089 _0.116** _0.379*** _0.096* _0.095* _1.000

Weather (11) -0.063 0.222*** _0.035 _0.061 _0.013 _0.089 _0.072 _-0.063 _0.013 _0.047 _1.000

Risk tolerance (12) -0.015 0.060 0.044 0.049 -0.079 0.025 -0.050 -0.053 0.089 -0.094* _-0.033 _1.000

Optimism (13) -0.035 0.301*** _0.002 _-0.009 _-0.007 _0.029 _0.014 _0.079 _-0.059 _0.077 _0.084 _0.023 _1.000

This table presents the correlation coefficients among the variables used in the different estimation models. Whereby, *_, **_{, and}***_{, indicate significance at the 10%, 5%, and}

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4. Methodology

The aim of this study is to determine if a relationship between an individual’s happiness and their investment in financial assets exists. This study will use multiple estimation techniques and models in order to check the existence of this relationship. The linear probability model and the logit model will be executed in order to test if happier individuals are more likely to invest in financial assets. The ordinary least square (OLS) and the Heckman two step model will be used to determine if happier individuals invest a higher amount of their wealth into financial assets. In order to check the robustness of these models, multiple control variables will be added to the estimation equations. First, control variables regarding the weather and optimism will be added. Second, some basic socioeconomic variables such as, gender, age, having children, marital state, education, employment, retirement, and income, and a more specific control variables which is risk tolerance, will be added. As mentioned in the data section these variables may influence the propensity and the amount an individual invests in financial assets.

4.1 Effect of happiness on the propensity to invest in financial assets

To test whether or not happier individuals have a higher propensity to invest in financial assets, a linear probability model will be estimated, where the impact of happiness is measured on a binary outcome. Here, the binary outcome equals 1 if an individual has investments in financial assets, and 0 otherwise. The specification of the linear probability model are as follows:

"#$ = &((#$ = 1) = + + -. + /#$ (1)

Where "_#$ is the probability that (_#$ is 1, (_#$ is a dummy variable with a value of 1 if an individual has investments financial assets, and a value of 0 otherwise, + is the constant, X is a vector of explanatory variables, . is a vector of coefficients, and / is the error term.

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values will fall within the bound of 0 and 1. Both the logit and the probit model will give very similar characteristics of the data because the densities are very similar (Brooks, 2004). This study will use a logit model to test the influence of the happiness creating variables on the propensity to invest. The specification of the logit model are as follows:

"_#$ = 0

01 23(45 6758) (2)

Where "_#$ is the probability that (_#$ = 1, + is the constant, X is a vector of explanatory variables, . is a vector of coefficients, and / is the error term.

4.2 Effect of happiness on the amount invested in financial assets

In order to test whether or not happier individuals invest more in financial assets there will be executed an ordinary least squared (OLS). The specification of the OLS model are as follows:

9 = + + -. + / (3)

Where Y is the natural logarithm of the amount of money that is invested in financial assets, + is the constant, X is a vector of explanatory variables, . is a vector of coefficients, and / is the error term.

However, estimating the investment in financial assets by OLS from a sample of only individuals that invest in financial assets, may lead to sample selection bias (Heckman, 1979). The Sample selection bias can be caused by two different issues. The first issue concerns itself with whether or not an individual invests. Individuals that do not invest are not able to report their investments, as they have none. It might be the case that there are unobservable characteristics that influence an individual’s decision to invest. In example, an unobservable characteristic like bravery may increase an individual’s propensity to invest. Therefor it might be the case that the sample is non-random and thus not a good representation of the population. The second issue is in regard to whether or not an individual is willing to report his or her investment. This non-response does not automatically bias the model as long as the sample of non-responders is randomly distributed. However, if the sample of non-responders is not random it might be the case that there are unobservable characteristics that influence an individual’s decision to report his or her investment. In example, an unobservable characteristic like superstition may decrease an individual’s propensity to report investments.

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between the error term (the unobserved characteristics) and the dependent variable is positive or negative (Certo, Busenbark, Woo, and Semadeni, 2016).

Because the sample used in this study consists only of individual’s that do report their investments, it most likely suffers from at least one of these two issues. In order to correct this sample selection bias, Heckman designed a two-step model. Within this model, the first step estimates what the likelihood is that an individual invests and calculates the inverse Mills ratio. When the coefficient of the inverse Mills ratio is statistically different from zero, there is evidence of sample selection. If there is a sample selection bias present normal OLS results are inconsistent and biased. Therefore, in order to correct this bias, the inverse Mills ratio is added to the investment equation as an explanatory variable in the second step. The specification of the Heckman two-step model is as follows:

";<=(> = 1|@) = A(@B) (4)

Where z indicates investment (Z = 1 if the respondent has investments in financial assets and Z = 0 otherwise), w is a vector of explanatory variables, B is a vector of coefficients, and A is the cumulative distribution function of the standard normal distribution.

D(@B) = E(FG)_H(FG) (5)

Where D(@B) is the inverse Mills ratio, φ(wγ) is the probability density function, and Φ(wγ) is the cumulative distribution function.

N((|-, > = 1) = + + -. + D(@B) + / (6)

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5. Results.

5.1. The effect of happiness on the propensity to invest in financial assets

The results of the regression of happiness on the dummy variable of whether or not an individual invests into financial assets are presented in table 3. The uneven columns represent a linear probability model and the even columns represent a logit model. In column (1) and (2) the effect of happiness on the propensity to invest in financial assets is measured without any control variables. The columns (3) and (4) have a control variable added for the weather and a control variable added for optimism to the model. In the columns (5) and (6) there are socio-economic and more specific control variables added, which are gender, age, having children, marital state, education, employment, retirement, net income, and risk tolerance. The dependent variable is a dummy variable for whether or not an individual has investments in financial assets.

For all the different models the happiness variable coefficient is positive, indicating that happiness has a positive effect on an individual’s decision to invest. This implies that happier individuals are more likely to invest than less happy individuals. These findings are in line with the Affect Infusion Model, which claims that individuals in a good mood have a higher tendency to take risk. However, none of these results are statistically significant, so it is not possible to reject the null hypothesis that happier individuals have a higher propensity to invest.

When looking at the results of the control variables it can be noticed that being a male has positive coefficients and is statistically significant in columns (5) and (6). This implies that being male has a positive influence on an individual’s decision to invest in financial assets. Education has a positive coefficient and is statistically significant in columns (5) and (6). This indicates that a higher education makes an individual more likely to invest in financial assets. When looking if an individual has children living at home, the variable has a negative coefficient and is statistically significant columns (5) and (6). This Means that when there are children living at home the odds that an individual will invest in financial assets decreases. Table 3. The effect of happiness on the propensity to invest in financial assets

LPM Logit LPM Logit LPM Logit

Fin. assets Fin. assets Fin. assets Fin. assets Fin. assets Fin. assets

(1) (2) (3) (4) (5) (6) Happiness 0.008 0.031 0.006 0.024 0.012 0.053 (0.028) (0.113) (0.030) (0.119) (0.033) (0.143) Constant 0.459*** _-0.165 _0.439*** _-0.243 _-0.078 _-2.521* (0.100) (0.399) (0.127) (0.508) (0.306) (1.364) Number of obs. 755 755 755 755 621 621

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Table 4. The effect of happiness on the propensity to invest in financial assets

(1) (2) (3) (4) (5) (6) Male 0.030 0.121 0.018 0.072 0.039 0.174 (0.036) (0.145) (0.039) (0.155) (0.041) (0.182) Female -0.037 -0.156 -0.032 -0.135 -0.028 -0.139 (0.044) (0.183) (0.046) (0.192) (0.052) (0.224) Married 0.018 0.074 0.019 0.075 0.024 0.108 (0.036) (0.145) (0.038) (0.152) (0.042) (0.185) Not married 0.000 0.001 -0.008 -0.031 -0.001 -0.007 (0.046) (0.184) (0.049) (0.195) (0.055) (0.235) Employed 0.012 0.048 0.011 0.043 -0.003 -0.013 (0.039) (0.160) (0.041) (0.165) (0.044) (0.187) Unemployed 0.009 0.038 -0.002 -0.006 0.023 0.108 (0.041) (0.165) (0.044) (0.178) (0.049) (0.218) Age < 35 0.027 0.110 -0.028 -0.134 0.003 0.020 (0.101) (0.414) (0.098) (0.421) (0.136) (0.557) Age 35 – 50 -0.081 -0.335 -0.051 -0.218 -0.048 -0.223 (0.055) (0.228) (0.059) (0.252) (0.069) (0.300) Age 50 - 65 0.059 0.239 0.056 0.230 0.047 0.212 (0.045) (0.184) (0.047) (0.194) (0.050) (0.223) Age > 65 0.050 0.209 0.032 0.142 0.087 0.459 (0.052) (0.217) (0.054) (0.227) (0.062) (0.305)

The coefficients represent the happiness coefficients belonging to the corresponding subsample in the most left column. The full tables of the different subsamples are displayed in the appendix. Data from the LISS panel data. The dependent variable is a binary indicator that takes a value of one when an individual has investment in financial assets, and 0 otherwise. The independent variable in columns 1 and 2 is happiness. In columns 3 and 4 happiness, weather, and optimism are the independent variables. Columns 5 and 6 use happiness, weather, optimism, gender, age, having children, married, education, employment, retirement, income, and risk tolerance as independent variables. Standard errors in parentheses. *_{p < 0.10,}**_{p < 0.05,}***_{p < 0.01.}

5.2. The effect of happiness on the amount invested in financial assets

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non-irrelevant for predicting a possible sample selection bias. Meaning that a possible sample selection bias could still be present

For all the different models the happiness variable coefficient is positive, indicating that happiness has a positive effect on an individual’s amount of wealth invested. This implies that happier individuals invest a higher amount of wealth compared to less happy individuals. These findings are in line with the Affect Infusion Model, which claims that individuals in a good mood have a higher tendency to take risk. However, none of these results are statistically significant, so it is not possible to reject the null hypothesis that happies individuals have a higher amount of wealth invested.

Observing the differences between OLS and Heckman two step it is clear that OLS gives higher coefficients in all three of the cases. This may indicate that results of OLS have an upwards bias. When looking at the results of the control variables, it can be noticed that age has a positive coefficient and is statistically significant in column (5). This implies that when an individual gets older, he or she invest more of its wealth into financial assets. In addition, risk tolerance, log net income, and education also have positive and statistically significant coefficient in column (5). This implies that an individual that is less risk averse, has a higher income, or has a better education, respectively, invests more into financial assets. Being a male has a negative and statistically significant coefficient in column (5). This would imply that males invest less of their wealth into financial assets than that females do.

Table 5. The effect of happiness on the amount invested in financial assets

OLS Heckman OLS Heckman OLS Heckman

(1) (2) (3) (4) (5) (6) Happiness 0.154 0.129 0.204 0.171 0.146 0.026 (0.152) (0.185) (0.158) (0.198) (0.142) (0.543) Constant 9.263*** _10.798*** _9.689*** _11.139*** _3.218** _13.769 (0.540) (0.810) (0.686) (0.964) (1.568) (25.419) Mills -2.041*** _-2.063*** _-5.966 (0.575) (0.578) (14.046) Number of obs. 367 621 367 621 326 621

The full table is displayed in the appendix. Data from the LISS panel data. The dependent variable is the natural logarithm of the amount invested in financial assets. The independent variable in columns (1) and (2) is happiness. In columns (3) and (4) happiness, weather, and optimism are the independent variables. Columns (5) and (6) use happiness, weather, optimism, gender, age, having children, married, education, employment, retirement, income, and risk tolerance as independent variables. For the selection model the variables happiness, weather, optimism, gender, age, having children, married, education, employment, retirement, income, and risk tolerance are used. Standard errors in parentheses. *_{p < 0.10,}**_{p < 0.05,}***_{p < 0.01.}

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the sub sample for age under 35 and the subsample for age between 50 and 65 have positive and statistically significant coefficients in column (5). This indicates that individuals belonging to these age groups invest more of their wealth into financial assets. However, none of the subsamples have consistent significant happiness coefficients, and more control variables make the significance drop in most cases. Furthermore, due to the exclusion of observations some subsamples become relatively small. Therefore, one must be cautious with the interpretation of the results of table 6.

Table 6. The effect of happiness on the amount invested in financial assets

(1) (2) (3) (4) (5) (6) Male 0.099 -0.018 0.068 -0.018 0.053 0.902 (0.184) (0.227) (0.202) (0.242) (0.170) (2.706) Female 0.272 0.470 0.428* _0.595* _0.265 _0.752 (0.261) (0.365) (0.240) (0.361) (0.305) (2.921) Married 0.074 0.038 0.095 0.038 0.169 -0.051 (0.220) (0.262) (0.233) (0.279) (0.183) (0.719) Not married 0.272 0.470 0.428* _0.595* _0.265 _0.752 (0.261) (0.365) (0.240) (0.361) (0.305) (2.921) Employed 0.184 0.217 0.282 0.306 0.135 0.157 (0.195) (0.193) (0.191) (0.204) (0.172) (0.380) Unemployed 0.163 0.162 0.147 0.124 0.059 0.581 (0.221) (0.276) (0.245) (0.306) (0.247) (2.276) Age < 35 0.581* _0.676* _0.365 _0.445 _1.470** _1.450 (0.307) (0.363) (0.462) (0.400) (0.430) (1.255) Age 35 – 50 0.293 0.250 0.367 0.325 -0.121 -0.457 (0.357) (0.355) (0.354) (0.392) (0.402) (1.050) Age 50 - 65 0.240 0.271 0.292 0.310 0.530** _1.094 (0.231) (0.252) (0.233) (0.265) (0.231) (2.159) Age > 65 -0.207 -0.128 -0.269 -0.301 -0.538 0.944 (0.235) (0.352) (0.266) (0.392) (0.350) (4.222)

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6. Conclusion

Due to the increasing personal responsibility among individuals regarding financial resources and thus financial decision making, a broader understanding of the decision-making process is important to economists and policy makers. This paper investigates the relationship between an individual’s level of happiness and its investments in financial assets. This paper is based on a representative sample of the Dutch population, obtained from the LISS panel dataset administered by CentERdata, and the statistical tests are conducted on a sample of 755 individual investors. Due to the nature of the sample it might suffer from a possible sample selection bias. Therefore, this study adopted the Heckman two step model in order to correct this sample selection bias. Furthermore, proxies for the effect of the weather, optimism, and risk tolerance are added to the model in order to mitigate any potential endogeneities that exist in the model.

The results show a positive effect of happiness on the propensity to invest. This implies that happier individuals are more likely to invest than less happy individuals. In addition, the results also show that happiness positively influences the amount of wealth invested. This would indicate that happier individuals invest a higher amount of wealth compared to less happy individuals. These results were controlled for by various socio-economic variables and proxies for the effect of the weather, optimism, and risk tolerance. These findings are in line with the Affect Infusion Model, which claims that individuals in a good mood have a higher tendency to take risk. However, none of these results are statistically significant, so one should be cautious with its interpretations of the results.

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Appendix

Table A1. The effect of happiness on the propensity to invest in financial assets

(1) (2) (3) (4) (5) (6) Happiness 0.008 0.031 0.006 0.024 0.012 0.053 (0.028) (0.113) (0.030) (0.119) (0.033) (0.143) Weather 0.023 0.094 0.008 0.035 (0.023) (0.093) (0.025) (0.109) Optimism -0.013 -0.051 -0.011 -0.047 (0.027) (0.109) (0.030) (0.128) Male 0.106** _0.449** (0.048) (0.202) Age 0.001 0.003 (0.002) (0.009) Children -0.143** _-0.597*** (0.051) (0.214) Married -0.073 -0.309 (0.044) (0.189) Education 0.026* _0.112* (0.014) (0.062) Employed -0.015 -0.065 (0.064) (0.278) Retired 0.049 0.207 (0.071) (0.304) Net income 0.056 0.247 (0.043) (0.192) Risk tolerance 0.009 0.039 (0.011) (0.047) Constant 0.459*** _-0.165 _0.439*** _-0.243 _-0.078 _-2.521* (0.100) (0.399) (0.127) (0.508) (0.306) (1.364) Number of obs. 755 755 755 755 621 621

Table A2. The effect of happiness on the propensity to invest in financial assets male subsample

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Employed -0.164* _-0.745* (0.085) (0.397) Retired -0.041 -0.202 (0.094) (0.443) Net income 0.106* _0.483* (0.058) (0.273) Risk tolerance 0.010 0.045 (0.013) (0.058) Constant 0.436*** _-0.257 _0.353** _-0.592 _-0.344 _-3.836** (0.128) (0.514) (0.158) (0.638) (0.416) (1.936) Number of obs. 484 484 484 484 410 410

Table A3. The effect of happiness on the propensity to invest in financial assets female subsample

(1) (2) (3) (4) (5) (6) Happiness -0.037 -0.156 -0.032 -0.135 -0.028 -0.139 (0.044) (0.183) (0.046) (0.192) (0.052) (0.224) Weather 0.035 0.150 0.002 0.011 (0.035) (0.149) (0.041) (0.176) Optimism -0.068 -0.292 -0.046 -0.197 (0.046) (0.197) (0.052) (0.226) Gender 0.000 0.000 (.) (.) Age 0.003 0.016 (0.004) (0.016) Children -0.115 -0.520 (0.090) (0.395) Married -0.129* _-0.538* (0.077) (0.327) Education 0.018 0.080 (0.025) (0.104) Employed 0.172* _0.802* (0.095) (0.447) Retired 0.117 0.514 (0.114) (0.483) Net income -0.023 -0.098 (0.067) (0.285) Risk tolerance 0.006 0.027 (0.019) (0.081) Constant 0.516*** _0.078 _0.628*** _0.555 _0.563 _0.163 (0.154) (0.639) (0.210) (0.881) (0.521) (2.244) Number of obs. 271 271 271 271 211 211

Table A4. The effect of happiness on the propensity to invest in financial assets married subsample

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0.072 0.296 Age (0.068) (0.296) 0.004 0.015 Children (0.003) (0.014) -0.046 -0.192 Married (0.065) (0.277) 0.000 0.000 Education (.) (.) 0.042** _0.183** Employed (0.017) (0.076) -0.102 -0.452 Retired (0.088) (0.389) -0.006 -0.036 Net income (0.091) (0.395) 0.087 0.398 Risk tolerance (0.055) (0.262) 0.010 0.045 Constant (0.014) (0.059) 0.396*** _-0.419 _0.378** _-0.491 _-0.578 _-4.835** Number of obs. (0.128) (0.517) (0.164) (0.661) (0.443) (2.095) Happiness 496 496 496 496 401 401

Table A5. The effect of happiness on the propensity to invest in financial assets not married subsample

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Table A6. The effect of happiness on the propensity to invest in financial assets employed subsample

(1) (2) (3) (4) (5) (6) Happiness 0.012 0.048 0.011 0.043 -0.003 -0.013 (0.039) (0.160) (0.041) (0.165) (0.044) (0.187) Weather -0.020 -0.081 -0.019 -0.083 (0.032) (0.128) (0.033) (0.138) Optimism 0.015 0.060 0.013 0.053 (0.035) (0.143) (0.038) (0.160) Gender 0.065 0.273 (0.066) (0.275) Age 0.002 0.008 (0.003) (0.012) Children -0.161*** _-0.668*** (0.060) (0.248) Married -0.110* _-0.462* (0.063) (0.262) Education 0.035 0.148 (0.021) (0.091) Employed 0.000 0.000 (.) (.) Retired 0.000 0.000 (.) (.) Net income 0.013 0.058 (0.066) (0.281) Risk tolerance 0.005 0.022 (0.016) (0.066) Constant 0.405*** _-0.384 _0.417** _-0.335 _0.265 _-1.033 (0.140) (0.568) (0.179) (0.724) (0.477) (2.034) Number of obs. 406 406 406 406 337 337

Table A7. The effect of happiness on the propensity to invest in financial assets unemployed subsample

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(0.080) (0.346) Net income 0.089 0.402 (0.057) (0.275) Risk tolerance 0.013 0.059 (0.016) (0.071) Constant 0.501*** _0.002 _0.466** _-0.142 _-0.469 _-4.354** (0.145) (0.579) (0.186) (0.756) (0.419) (1.990) Number of obs. 349 349 349 349 284 284

Table A8. The effect of happiness on the propensity to invest in financial assets age under 35 subsample

(1) (2) (3) (4) (5) (6) Happiness 0.027 0.110 -0.028 -0.134 0.003 0.020 (0.101) (0.414) (0.098) (0.421) (0.136) (0.557) Weather 0.116 0.504 -0.004 0.192 (0.072) (0.333) (0.121) (0.951) Optimism 0.073 0.327 0.004 0.348 (0.077) (0.353) (0.098) (0.659) Gender 0.360* _2.146** (0.188) (0.876) Age -0.011 -0.108 (0.021) (0.124) Children -0.451* _-3.072 (0.256) (2.690) Married 0.028 0.268 (0.203) (1.111) Education 0.202 1.064 (0.135) (0.653) Employed -0.174 -0.958 (0.440) (2.136) Retired 0.000 0.000 (.) (.) Net income -0.270 -1.448 (0.258) (1.301) Risk tolerance 0.049 0.277 (0.042) (0.212) Constant 0.331 -0.690 -0.074 -2.490 1.751 6.275 (0.359) (1.478) (0.407) (1.819) (1.429) (7.191) Number of obs. 59 59 59 59 38 38

Table A9. The effect of happiness on the propensity to invest in financial assets age between 35 and 50 subsample

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(0.009) (0.041) Children -0.230** _-1.000** (0.103) (0.438) Married -0.009 -0.044 (0.096) (0.408) Education 0.018 0.086 (0.032) (0.140) Employed 0.042 0.216 (0.141) (0.610) Retired 0.000 0.000 (.) (.) Net income -0.019 -0.088 (0.088) (0.387) Risk tolerance 0.023 0.104 (0.024) (0.103) Constant 0.697*** _0.817 _0.972*** _2.013* _1.121 _2.859 (0.191) (0.790) (0.249) (1.090) (0.683) (3.037) Number of obs. 181 181 181 181 149 149

Table A10. The effect of happiness on the propensity to invest in financial assets age between 50 and 65 subsample

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Table A11. The effect of happiness on the propensity to invest in financial assets age above 65 subsample

(1) (2) (3) (4) (5) (6) Happiness 0.050 0.209 0.032 0.142 0.087 0.459 (0.052) (0.217) (0.054) (0.227) (0.062) (0.305) Weather 0.083* _0.355* _0.029 _0.146 (0.045) (0.199) (0.052) (0.246) Optimism -0.023 -0.103 -0.079 -0.386 (0.058) (0.246) (0.065) (0.315) Gender 0.168 0.780 (0.109) (0.499) Age 0.001 0.002 (0.006) (0.030) Children -0.757*** _0.000 (0.119) (.) Married -0.070 -0.378 (0.087) (0.429) Education -0.000 -0.002 (0.026) (0.126) Employed -0.094 -0.537 (0.199) (1.282) Retired -0.120 -0.607 (0.134) (0.685) Net income 0.153* _0.701 (0.092) (0.435) Risk tolerance 0.015 0.079 (0.020) (0.099) Constant 0.434** _-0.285 _0.334 _-0.723 _-0.718 _-5.391 (0.182) (0.756) (0.243) (1.030) (0.784) (3.782) Number of obs. 203 203 203 203 172 170

Table A12. The effect of happiness on the amount invested in financial assets

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(0.410) (1.471)

Log net income 0.582** _0.014

(0.247) (1.496) Risk tolerance 0.127** _0.040 (0.059) (0.257) Constant 9.263*** _10.798*** _9.689*** _11.139*** _3.218** _13.769 (0.540) (0.810) (0.686) (0.964) (1.568) (25.419) Selection Happiness 0.030 0.030 0.030 (0.085) (0.085) (0.085) Male 0.277** _0.277** _0.277** (0.123) (0.123) (0.123) Age 0.002 0.002 0.002 (0.006) (0.006) (0.006) Children -0.370*** _-0.370*** _-0.370*** (0.132) (0.132) (0.132) Married -0.187 -0.187 -0.187 (0.117) (0.117) (0.117) Education 0.069* _0.069* _0.069* (0.038) (0.038) (0.038) Employed -0.038 -0.038 -0.038 (0.172) (0.172) (0.172) Retired 0.127 0.127 0.127 (0.189) (0.189) (0.189)

Log net income 0.150 0.150 0.150

(0.113) (0.113) (0.113) Weather 0.021 0.021 0.021 (0.066) (0.066) (0.066) Risk tolerance 0.024 0.024 0.024 (0.028) (0.028) (0.028) Optimism -0.027 -0.027 -0.027 (0.077) (0.077) (0.077) Constant -1.536* _-1.536* _-1.536* (0.797) (0.797) (0.797) Mills -2.041*** _-2.063*** _-5.966 (0.575) (0.578) (14.046) Number of obs. 367 621 367 621 326 621

Table 13A. The effect of happiness on the amount invested in financial assets male subsample

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Education 0.205* _0.888 (0.109) (1.972) Employed -0.023 -3.451 (0.393) (9.857) Retired 0.121 -0.724 (0.401) (3.877) Net income 0.883*** _3.152 (0.316) (6.594) Risk tolerance 0.118* _0.340 (0.064) (0.754) Constant 9.431*** _11.345*** _9.244*** _11.420*** _-0.004 _-29.523 (0.652) (1.017) (0.785) (1.218) (2.010) (82.374) Selection Happiness 0.108 0.108 0.108 (0.110) (0.110) (0.110) Weather 0.000 0.000 0.000 (.) (.) (.) Optimism -0.001 -0.001 -0.001 (0.007) (0.007) (0.007) Gender -0.361** _-0.361** _-0.361** (0.161) (0.161) (0.161) Age -0.188 -0.188 -0.188 (0.156) (0.156) (0.156) Children 0.087* _0.087* _0.087* (0.048) (0.048) (0.048) Married -0.464* _-0.464* _-0.464* (0.239) (0.239) (0.239) Education -0.126 -0.126 -0.126 (0.259) (0.259) (0.259) Employed 0.298* _0.298* _0.298* (0.161) (0.161) (0.161) Retired 0.018 0.018 0.018 (0.084) (0.084) (0.084) Net income 0.027 0.027 0.027 (0.035) (0.035) (0.035) Risk tolerance 0.017 0.017 0.017 (0.095) (0.095) (0.095) Constant -2.371** _-2.371** _-2.371** (1.135) (1.135) (1.135) Mills -2.365*** _-2.378*** _14.105 (0.736) (0.743) (38.379) Number of obs. 262 410 262 410 236 410

Table 14A. The effect of happiness on the amount invested in financial assets female subsample

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(0.023) (0.328) Children -1.308* _0.587 (0.697) (10.511) Married 0.386 2.196 (0.537) (10.008) Education 0.043 -0.225 (0.143) (1.566) Employed -1.370* _-4.220 (0.797) (15.793) Retired -0.997 -2.795 (0.996) (10.081) Net income 0.150 0.464 (0.491) (2.323) Risk tolerance 0.075 -0.021 (0.152) (0.674) Constant 8.921*** _9.870*** _11.080*** _11.143*** _10.001** _16.628 (0.926) (1.481) (1.375) (1.722) (3.877) (38.299) Selection Happiness -0.085 -0.085 -0.085 (0.140) (0.140) (0.140) Weather 0.000 0.000 0.000 (.) (.) (.) Optimism 0.010 0.010 0.010 (0.010) (0.010) (0.010) Gender -0.318 -0.318 -0.318 (0.244) (0.244) (0.244) Age -0.330 -0.330 -0.330 (0.203) (0.203) (0.203) Children 0.048 0.048 0.048 (0.065) (0.065) (0.065) Married 0.492* _0.492* _0.492* (0.273) (0.273) (0.273) Education 0.313 0.313 0.313 (0.292) (0.292) (0.292) Employed -0.060 -0.060 -0.060 (0.181) (0.181) (0.181) Retired 0.002 0.002 0.002 (0.107) (0.107) (0.107) Net income 0.016 0.016 0.016 (0.050) (0.050) (0.050) Risk tolerance -0.122 -0.122 -0.122 (0.140) (0.140) (0.140) Constant 0.116 0.116 0.116 (1.404) (1.404) (1.404) Mills -1.857 -1.403 -8.563 (1.140) (1.195) (46.577) Number of obs. 105 211 105 211 90 211

Table 15A. The effect of happiness on the amount invested in financial assets married subsample

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Table 16A. The effect of happiness on the amount invested in financial assets not married subsample

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(0.133) (0.133) (0.133) Constant -0.198 -0.198 -0.198 (1.378) (1.378) (1.378) Mills -0.959 -0.915 -22.512 (0.961) (0.954) (146.941) Number of obs. 139 220 139 220 126 220

Table 17A. The effect of happiness on the amount invested in financial assets employed subsample

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(0.172) (0.172) (0.172) Retired -0.052 -0.052 -0.052 (0.090) (0.090) (0.090) Net income 0.013 0.013 0.013 (0.039) (0.039) (0.039) Risk tolerance 0.034 0.034 0.034 (0.100) (0.100) (0.100) Constant -0.629 -0.629 -0.629 (1.257) (1.257) (1.257) Mills -1.056 -1.148* _-3.584 (0.651) (0.658) (14.943) Number of obs. 181 337 181 337 163 337

Table 18A. The effect of happiness on the amount invested in financial assets unemployed subsample

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(0.053) (0.053) (0.053) Married 0.000 0.000 0.000 (.) (.) (.) Education 0.085 0.085 0.085 (0.211) (0.211) (0.211) Employed 0.246 0.246 0.246 (0.157) (0.157) (0.157) Retired 0.087 0.087 0.087 (0.101) (0.101) (0.101) Net income 0.034 0.034 0.034 (0.041) (0.041) (0.041) Risk tolerance -0.086 -0.086 -0.086 (0.125) (0.125) (0.125) Constant -2.666** _-2.666** _-2.666** (1.149) (1.149) (1.149) Mills -0.621 -1.153 14.206 (0.793) (0.841) (39.748) Number of obs. 186 284 186 284 163 284

Table 19A. The effect of happiness on the amount invested in financial assets age under 35 subsample

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(0.101) (0.101) (0.101) Gender -1.550* _-1.550* _-1.550* (0.817) (0.817) (0.817) Age 0.267 0.267 0.267 (0.813) (0.813) (0.813) Children 0.608 0.608 0.608 (0.386) (0.386) (0.386) Married -0.401 -0.401 -0.401 (1.390) (1.390) (1.390) Education 0.000 0.000 0.000 (.) (.) (.) Employed -0.830 -0.830 -0.830 (0.850) (0.850) (0.850) Retired -0.008 -0.008 -0.008 (0.385) (0.385) (0.385) Net income 0.159 0.159 0.159 (0.149) (0.149) (0.149) Risk tolerance 0.164 0.164 0.164 (0.428) (0.428) (0.428) Constant 3.755 3.755 3.755 (5.371) (5.371) (5.371) Mills 0.238 0.428 2.762 (0.673) (0.680) (9.456) Number of obs. 25 38 25 38 19 38

Table 20A. The effect of happiness on the amount invested in financial assets age between 35 and 50 subsample